Unsupervised Learning of Fine Structure Generation for 3D Point Clouds by 2D Projection Matching

• URL: http://arxiv.org/abs/2108.03746v1
• Date: Sun, 8 Aug 2021 22:15:31 GMT
• Title: Unsupervised Learning of Fine Structure Generation for 3D Point Clouds by 2D Projection Matching
• Authors: Chen Chao and Zhizhong Han and Yu-Shen Liu and Matthias Zwicker
• Abstract summary: We propose an unsupervised approach for 3D point cloud generation with fine structures. Our method can recover fine 3D structures from 2D silhouette images at different resolutions.
• Score: 66.98712589559028
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning to generate 3D point clouds without 3D supervision is an important but challenging problem. Current solutions leverage various differentiable renderers to project the generated 3D point clouds onto a 2D image plane, and train deep neural networks using the per-pixel difference with 2D ground truth images. However, these solutions are still struggling to fully recover fine structures of 3D shapes, such as thin tubes or planes. To resolve this issue, we propose an unsupervised approach for 3D point cloud generation with fine structures. Specifically, we cast 3D point cloud learning as a 2D projection matching problem. Rather than using entire 2D silhouette images as a regular pixel supervision, we introduce structure adaptive sampling to randomly sample 2D points within the silhouettes as an irregular point supervision, which alleviates the consistency issue of sampling from different view angles. Our method pushes the neural network to generate a 3D point cloud whose 2D projections match the irregular point supervision from different view angles. Our 2D projection matching approach enables the neural network to learn more accurate structure information than using the per-pixel difference, especially for fine and thin 3D structures. Our method can recover fine 3D structures from 2D silhouette images at different resolutions, and is robust to different sampling methods and point number in irregular point supervision. Our method outperforms others under widely used benchmarks. Our code, data and models are available at https://github.com/chenchao15/2D\_projection\_matching.

Related papers

• Robust 3D Point Clouds Classification based on Declarative Defenders [18.51700931775295]
  3D point clouds are unstructured and sparse, while 2D images are structured and dense. In this paper, we explore three distinct algorithms for mapping 3D point clouds into 2D images. The proposed approaches demonstrate superior accuracy and robustness against adversarial attacks.
  arXiv  Detail & Related papers  (2024-10-13T01:32:38Z)
• EP2P-Loc: End-to-End 3D Point to 2D Pixel Localization for Large-Scale Visual Localization [44.05930316729542]
  We propose EP2P-Loc, a novel large-scale visual localization method for 3D point clouds. To increase the number of inliers, we propose a simple algorithm to remove invisible 3D points in the image. For the first time in this task, we employ a differentiable for end-to-end training.
  arXiv  Detail & Related papers  (2023-09-14T07:06:36Z)
• Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors [104.79392615848109]
  We present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes from a single unposed image. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture.
  arXiv  Detail & Related papers  (2023-06-30T17:59:08Z)
• Leveraging Single-View Images for Unsupervised 3D Point Cloud Completion [53.93172686610741]
  Cross-PCC is an unsupervised point cloud completion method without requiring any 3D complete point clouds. To take advantage of the complementary information from 2D images, we use a single-view RGB image to extract 2D features. Our method even achieves comparable performance to some supervised methods.
  arXiv  Detail & Related papers  (2022-12-01T15:11:21Z)
• PointMCD: Boosting Deep Point Cloud Encoders via Multi-view Cross-modal Distillation for 3D Shape Recognition [55.38462937452363]
  We propose a unified multi-view cross-modal distillation architecture, including a pretrained deep image encoder as the teacher and a deep point encoder as the student. By pair-wise aligning multi-view visual and geometric descriptors, we can obtain more powerful deep point encoders without exhausting and complicated network modification.
  arXiv  Detail & Related papers  (2022-07-07T07:23:20Z)
• CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding [2.8661021832561757]
  CrossPoint is a simple cross-modal contrastive learning approach to learn transferable 3D point cloud representations. Our approach outperforms the previous unsupervised learning methods on a diverse range of downstream tasks including 3D object classification and segmentation.
  arXiv  Detail & Related papers  (2022-03-01T18:59:01Z)
• ParaNet: Deep Regular Representation for 3D Point Clouds [62.81379889095186]
  ParaNet is a novel end-to-end deep learning framework for representing 3D point clouds. It converts an irregular 3D point cloud into a regular 2D color image, named point geometry image (PGI) In contrast to conventional regular representation modalities based on multi-view projection and voxelization, the proposed representation is differentiable and reversible.
  arXiv  Detail & Related papers  (2020-12-05T13:19:55Z)
• From Image Collections to Point Clouds with Self-supervised Shape and Pose Networks [53.71440550507745]
  Reconstructing 3D models from 2D images is one of the fundamental problems in computer vision. We propose a deep learning technique for 3D object reconstruction from a single image. We learn both 3D point cloud reconstruction and pose estimation networks in a self-supervised manner.
  arXiv  Detail & Related papers  (2020-05-05T04:25:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.